Without limiting the scope of the invention, its background is described in connection with computer-aided detection of breast cancer. The American Cancer Society estimates that 178,480 women will be diagnosed with breast cancer in the U.S. in 2007 [1] and 40,460 women will die of the disease. In the U.S., breast cancer is the most common form of cancer among women and is the second leading cause of cancer deaths, after lung cancer [1]. Women in the U.S. have about a 1 in 8 lifetime risk of developing invasive breast cancer [2,3]. Early detection of breast cancer increases the treatment options for patients and also increases the survival rate.
Screening mammography, or x-ray imaging of the breast, is currently one effective tool for early detection of breast cancer. Screening mammographic examinations are performed on asymptomatic woman to detect early, clinically unsuspected breast cancer. Two views of each breast are recorded: the craniocaudal (CC) view, which is a top to bottom view, and the mediolateral oblique (MLO) view, which is a side view. Radiologists visually search mammograms for specific abnormalities. The most common signs of breast cancer that radiologists look for are clusters of microcalcifications and masses. A mass is a space-occupying lesion seen in at least two different projections [4]. Masses with spiculated margins carry a much higher risk of malignancy than other types of masses or calcifications. Spiculated masses account for about 14% of biopsied lesions, and about 81% of these are malignant [5].
Early detection via mammography increases breast cancer treatment options and the survival rate [6]. However, mammography is not perfect. Detection of suspicious abnormalities is a repetitive and fatiguing task. For every thousand cases analyzed by a radiologist, only 3 to 4 cases are malignant and thus an abnormality may be overlooked. As a result, radiologists fail to detect 10-30% of cancers [7-9]. Approximately two-thirds of these false-negative results are due to missed lesions that are evident retrospectively [10].
Computer-Aided Detection (CADe) systems have been developed to aid radiologists in detecting mammographic lesions that may indicate the presence of breast cancer [11-15]. These systems act as a second reader and the final decision is made by the radiologist. Most studies have shown that CADe systems, when used as an aid, improve radiologists' accuracy in the detection of breast cancer [16-18], though some studies have found no increase in the number of cancers detected [19].
Current CADe systems are dramatically better at detecting microcalcifications than masses. The most widely used commercial CADe system is reported to have a 98.5% sensitivity at 0.185 false positives per image (FPI) for microcalcification clusters and a 86% sensitivity at 0.24 FPI for spiculated masses [18]. However, the results vary considerably on different datasets. For example, clinical studies to evaluate the performance of commercial CADe systems for mass detection, have reported sensitivities ranging from 67% to 89% with the FPI ranging from 0.40 to 0.74 FPI [16,17,20-22]. For normal images FP rates of 1.3 to 1.8 FPI have been reported [22,23].
A number of references have focused on the detection of spiculated masses because of their high likelihood of malignancy. The main idea behind previous approaches to the detection of spiculated masses is that since they are characterized by spicules radiating in all directions, one should compute the edge orientations at each pixel. Thus, each pixel is represented by a feature vector, which represents the strongest edge orientation at the pixel. The edge orientation can be computed in a variety of different ways.
For example, Kegelmeyer et al. [24] developed a method to detect spiculated masses using a set of 5 features for each pixel. They used the standard deviation of a local edge orientation histogram (ALOE) and the output of four spatial filters, which are a subset of Law's texture features. The idea of using the ALOE feature is that, as a normal mammogram exhibits a tissue structure that radiates in a particular orientation (from the nipple to the chest), it would have edge orientations primarily in that direction. While in regions containing spiculated lesions, edges would exist in many different orientations. To detect these differences, Kegelmeyer et al. [24] computed the edge orientations in a window around each pixel and then generated a histogram of the edge orientations.
Another example can be seen in Karssemeijer et al. [25] where Karssemeijer detected spiculated masses by a statistical analysis of a map of pixel orientations. The orientation at each pixel was computed from the response of three filter kernels, which are second-order, directional derivatives of a Gaussian kernel in three directions (0,π/3,2π/3). These filters form a non-orthogonal basis. They used the relation that at a particular scale, the output at any orientation can be expressed as a weighted sum of the responses of the filters. This was used to determine the orientation at each pixel, and two features for each pixel were derived by a statistical analysis of these pixel orientation maps. The pixels were then classified as suspicious or normal.
Yet another example is found in Liu and Delp [26]. Liu and Delp noted that, in general, it is difficult to estimate the size of the neighborhood that should be used to compute the local features of spiculated masses. Small masses may be missed if the neighborhood is too large and parts of large masses may be missed if the neighborhood is too small. To address this problem they developed a multi-resolution algorithm for the detection of spiculated masses [26]. A multi-resolution representation of a mammogram using the Discrete Wavelet Transform was generated and four features at each resolution for each pixel were extracted. Pixels were then classified using a binary classification tree.
One can also see that Zhang et al. [27] noted that the presence of spiculated lesions led to changes in the local mammographic texture. They proposed that such a change could be detected in the Hough Domain, which is computed using the Hough Transform. They partitioned an image into overlapping ROIs and computed the Hough Transform for each ROI. The Hough Domain of each ROI was thresholded to detect local changes in the mammographic texture and to determine the presence or the absence of a spiculated mass.
Finally, Zwiggelaar et al. [28]proposed a model-based approach for the detection of spiculated masses. They described a technique to characterize patterns of linear structures using Principal Component Analysis and Factor Analysis. They created statistical models of spiculations created using regions-of-interest containing spiculated masses.
However, all of these references lack data on physical properties of spiculated masses and the old computer-aided algorithm generates many false positive detections. As a result, there is a need for a system and method for detecting spiculated masses in an image (e.g., mammogram that detects and enhances the spiculated masses while reducing the number of false positives.